Managing Pore-Water Quality in Mine Tailings by Inducing Microbial Sulfate Reduction
Bibliographic record
Abstract
A field-scale experiment was conducted to evaluate the potential for inducing microbial sulfate reduction as a passive in situ technique for managing water quality in mine tailings deposits. Sulfide- and carbonate-rich minetailings, characterized by near-neutral pH pore water, were amended with < 1 dry wt. % organic carbon. The geochemical evolution of pore water was monitored for four years. The results demonstrate that organic carbon supported dissimilatory sulfate reduction (DSR) in the vadose zone. Decreases in dissolved SO4 and S2O3 were accompanied by H2S production, increased populations of sulfate-reducing bacteria (SRB), 34S-SO4 enrichment, and undersaturation of pore water with respect to gypsum [CaSO4 x 2H2O]. The mass of dissolved S decreased by > 45% during the monitoring period, which coincided with the removal of Zn, Sb, and Tl. Mobilization of Fe and As occurred initially; however, subsequent decreases in aqueous concentrations were observed. Mineralogical investigation confirmed the presence of secondary Fe-S and Zn-Fe-S phases. Amendment of tailings with a small and dispersed mass of organic carbon resulted in a general decrease in mass transport of sulfide oxidation products.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".